SpongeFiles: Mitigating Data Skew in MapReduce Using Distributed Memory. Khaled Elmeleegy Turn Inc.

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kelmeleegy@turn.com Christopher Olston Google Inc.

1 SpongeFiles: Mitigating Data Skew in MapReduce Using Distributed Memory Khaled Elmeleegy Turn Inc. Christopher Olston Google Inc. Benjamin Reed Facebook Inc. 1

2 Background MapReduce are the primary platform for processing web & social networking data sets These data sets tend to be heavily skewed Native : hot news, hot people(e.g. holistic aggregation) Machine learning: unkonw topic unknow city Skew leads to overwhelm that node's memory capacity 2

3 Data Skew: harmness & solution Harmness The major slow down of the MR job Solution Provide sufficient memory Use data skew avoidance techniques 3

4 Solution1: Provide Sufficient Memory Method: Providing every task with enough memory Shortcoming: Tasks are only run-time known Required memory may not exist at the node executing the task Conclusion: Although can mitigate spilling, but very wasteful 4

5 Solution2: Data skew avoidance techniques Method: Skew-resistant partitioning schemas Skew detection and work migration techniques Shortcoming: UDFs may be vulnerable to data skew Conclusion: Alleviating some, but not all 5

6 Sometimes, We have to resort to Spill 6

7 Hadoop s Map Phase Spill kvindeces[] 3.75% By default 100MB p k v kvoffset[] 1.25% p k v p k v k v k v k v kvbuffer[] 95% 7

8 Hadoop s Reduce Phase Spill Memory buffer InMemFSMergerThread MapOutputCopier MapOutputCopier MapOutputCopier MapOutputCopier In Memory On Disk merge Spill to Disk LocalFSMerger copiers merge Local Disk 8

9 How we expect that we can share the memory 9

10 Here comes the SpongeFile Share memory in the same node Share memory between peers 10

11 SpongeFile Utilize remote idle memory Operates at app level (unlike remote memory) Single spilled obj stored in a single sponge file Composed of large chunks Be used to complement a process's memory pool Much simpler than regular files(for fast read & write ) 11

12 Design No concurrent single writer and a single Does not persist after it is read lifetime is well defined Do not need a naming service Each chunk can lie in the : machine's local memory, a remote machine's memory, a local file system, or a distributed file system 12

13 Local Memory Chunk Allocator Effect: Steps: Share memory between tasks in the same node 1. Acquires the shared pool's lock 2. Tries to find a free chunk 3. Release the lock & return the chunk handle (meta data) 4. Return a error if no free chunk 13

14 Remote Memory Chunk Allocator Effect: Share memory between tasks among peers Steps: 1. Get the list of sponge servers with free memory 2. Find a server with free space (on the same rack) 3. Writes data & gets back a handle 14

15 Disk Chunk Allocator Effect: Steps: It is the last resort, similar to spill on disk 1. Tries on the underlying local file system 2. If local disks have no free space, then tries the distributed file systems 15

16 Garbage Collection Tasks are alive: delete their SpongeFiles before they exit Tasks failed: sponge servers perform periodic garbage collections 16

17 Potential weakness analytics May Increase the probability of task failure But: = 1% per month N :number of machine t :running time MTTF : mean time to failure 17

18 Evaluation Microbenchmarks 2.5Ghz quad core Xeon CPUs 16GB of memory 7200RPM 300G ATA drives 1Gb Ethernet Red Hat Enterprise Linux Server release 5.3 Ext4 fs Macrobenchmarks Hadoop of 30 node(2 map task slots & 1 reduce) Pig 0.7 With above 18

19 Microbenchmarks Spill a 1 MB buffer 10,000 times to disk and mem In Memory On Disk category Time(ms) category Time(ms) Local shared memory 1 Disk 25 Local memory ( through sponge server) Remote memory (over the network 7 9 Disk with background IO Disk with background IO and memory pressure

20 Microbenchmarks s conclusion 1. spilling to shared memory is the least expensive 2. Then comes spilling locally via the local sponge(more processing and multiple messageexchanges) 3. Disk spilling is two orders of magnitude slower than memory 20

21 Macrobenchmarks The jobs data sets: Two versions of Hadoop: The original With SpongeFile Two configuration of memory size: 4GB 16GB 21

22 Test1 When memory size is small, spilling to SpongeFiles performs better than spilling to disk When memory is abundant, performance depends on the amount of data spilled and the time dierence between when the data is spilled and when it is read back 22

Test2 Using SpongeFiles reduces the job1 s runtime by over 85% in case of disk contention and memory pressure(similar behavior is seen for the spam quantiles)

23 Test2 Using SpongeFiles reduces the job1 s runtime by over 85% in case of disk contention and memory pressure(similar behavior is seen for the spam quantiles) For the frequent anchor text job, when memory is abundant and even with disk contention, spilling to disk performs slightly better than spilling to SpongeFiles. 23

24 Test3 No spilling performs best Spilling to local sponge memory performs second But spilling to SpongeFiles is the only one practical 24

25 Related work Cooperative caching (for share) Network memory (for small objects) Remote paging systems (not the same level) Conclusion Complementary to skew avoidance Reduce job runtimes by up to 55% in absence of disk contention and by up to 85% 25

Distributed Systems 16. Distributed File Systems II

Distributed Systems 16. Distributed File Systems II Paul Krzyzanowski pxk@cs.rutgers.edu 1 Review NFS RPC-based access AFS Long-term caching CODA Read/write replication & disconnected operation DFS AFS